Model Selection

Selecting the appropriate AI model is crucial for achieving optimal performance, cost-effectiveness, and user experience in your Talk Box applications. This guide covers the key considerations and trade-offs for different model types and providers.

Understanding Model Categories

Model Capabilities by Size

AI models fall into different capability tiers based on their parameter count and training:

import talk_box as tb

# Tier 1: Large, most capable models (best for complex reasoning)
advanced_models = [
    "gpt-4",           # OpenAI's most capable model
    "gpt-4-turbo",     # Faster, more cost-effective GPT-4
    "claude-3-opus",   # Anthropic's most capable model
    "gemini-pro",      # Google's advanced model
]

# Tier 2: Balanced models (good performance/cost ratio)
balanced_models = [
    "gpt-3.5-turbo",   # OpenAI's reliable balanced option (recommended default)
    "claude-3-sonnet", # Anthropic's balanced model
    "gemini-flash",    # Google's fast, efficient model
]

# Tier 3: Fast, lightweight models (best for simple tasks)
lightweight_models = [
    "gpt-3.5-turbo-16k",  # Extended context version
    "claude-3-haiku",     # Anthropic's fastest model
]

Model Characteristics Comparison

Model	Reasoning	Speed	Cost	Context Length	Best Use Cases
GPT-4	Excellent	Slow	High	8K-128K	Complex analysis, research, creative writing
GPT-4 Turbo	Excellent	Medium	Medium	128K	Production apps needing GPT-4 quality
GPT-4o	Excellent	Fast	Medium	128K	Multimodal tasks, excellent default choice
GPT-3.5 Turbo	Good	Fast	Low	4K-16K	General chatbots, quick responses
Claude 3 Opus	Excellent	Slow	High	200K	Long documents, detailed analysis
Claude 3 Sonnet	Very Good	Medium	Medium	200K	Balanced apps, content creation
Claude 3 Haiku	Good	Very Fast	Very Low	200K	High-volume, simple interactions
Gemini Pro	Very Good	Medium	Medium	32K	Multimodal tasks, Google ecosystem

Model Selection Framework

1. Define Your Requirements

Start by clearly defining your application’s needs:

class ModelRequirements:
    """Framework for defining model selection criteria."""

    def __init__(self):
        self.task_complexity = None      # simple, moderate, complex
        self.response_time_requirement = None  # <1s, <3s, <10s, flexible
        self.cost_sensitivity = None     # low, medium, high
        self.context_length_needed = None  # <4K, <16K, <128K, >128K
        self.accuracy_requirement = None  # acceptable, good, excellent
        self.volume_expectation = None   # <100/day, <1K/day, >1K/day

    def recommend_models(self):
        """Recommend models based on requirements."""
        recommendations = []

        if self.task_complexity == "complex":
            if self.cost_sensitivity == "low":
                recommendations.extend(["gpt-4", "claude-3-opus"])
            else:
                recommendations.extend(["gpt-4-turbo", "claude-3-sonnet"])

        elif self.task_complexity == "moderate":
            if self.response_time_requirement == "<1s":
                recommendations.extend(["gpt-3.5-turbo", "claude-3-haiku"])
            else:
                recommendations.extend(["gpt-4-turbo", "claude-3-sonnet"])

        elif self.task_complexity == "simple":
            recommendations.extend(["gpt-3.5-turbo", "claude-3-haiku"])

        return recommendations

# Example usage
requirements = ModelRequirements()
requirements.task_complexity = "moderate"
requirements.response_time_requirement = "<3s"
requirements.cost_sensitivity = "medium"
requirements.context_length_needed = "<16K"

recommended_models = requirements.recommend_models()
print(f"Recommended models: {recommended_models}")

2. Task-Specific Model Selection

Different tasks benefit from different model strengths:

def select_model_for_task(task_type: str) -> str:
    """Select optimal model based on task type."""

    task_model_map = {
        # Code-related tasks
        "code_review": "gpt-4",           # Needs deep reasoning
        "code_generation": "gpt-4-turbo", # Good balance of quality/speed
        "syntax_checking": "gpt-3.5-turbo", # Simple pattern matching

        # Analysis tasks
        "document_analysis": "claude-3-opus",   # Excellent with long text
        "data_interpretation": "gpt-4",         # Strong analytical reasoning
        "summarization": "claude-3-sonnet",     # Good balance for summaries

        # Creative tasks
        "creative_writing": "gpt-4",            # Best creative capabilities
        "content_editing": "claude-3-sonnet",   # Good language skills
        "translation": "gpt-4-turbo",          # Strong multilingual

        # Conversational tasks
        "customer_support": "gpt-3.5-turbo",   # Fast, cost-effective
        "general_chat": "claude-3-haiku",      # Very responsive
        "educational_tutor": "gpt-4-turbo",    # Needs good explanations

        # Technical tasks
        "api_documentation": "gpt-4",          # Needs technical accuracy
        "troubleshooting": "claude-3-sonnet",  # Good reasoning, fast
        "configuration": "gpt-3.5-turbo",     # Straightforward tasks
    }

    return task_model_map.get(task_type, "gpt-3.5-turbo")  # Default fallback

# Example specialized bots
code_reviewer = (tb.ChatBot()
    .model(select_model_for_task("code_review"))
    .persona("senior developer", "thorough code analysis")
    .temperature(0.3))  # Lower creativity for accuracy

content_creator = (tb.ChatBot()
    .model(select_model_for_task("creative_writing"))
    .persona("creative writer", "engaging content creation")
    .temperature(0.8))  # Higher creativity

support_bot = (tb.ChatBot()
    .model(select_model_for_task("customer_support"))
    .persona("helpful support agent", "quick problem resolution")
    .temperature(0.4))  # Balanced and consistent

3. Performance vs Cost Optimization

import time
from dataclasses import dataclass
from typing import List, Dict

@dataclass
class ModelPerformanceMetrics:
    """Track model performance metrics."""
    model_name: str
    avg_response_time: float
    cost_per_1k_tokens: float
    accuracy_score: float  # 0-1 scale
    context_limit: int

class ModelOptimizer:
    """Optimize model selection based on performance and cost."""

    def __init__(self):
        # Approximate metrics (update with real data)
        self.model_metrics = {
            "gpt-4": ModelPerformanceMetrics(
                model_name="gpt-4",
                avg_response_time=8.0,
                cost_per_1k_tokens=0.03,
                accuracy_score=0.95,
                context_limit=8192
            ),
            "gpt-4-turbo": ModelPerformanceMetrics(
                model_name="gpt-4-turbo",
                avg_response_time=4.0,
                cost_per_1k_tokens=0.01,
                accuracy_score=0.93,
                context_limit=128000
            ),
            "gpt-4o": ModelPerformanceMetrics(
                model_name="gpt-4o",
                avg_response_time=3.0,
                cost_per_1k_tokens=0.005,
                accuracy_score=0.94,
                context_limit=128000
            ),
            "gpt-3.5-turbo": ModelPerformanceMetrics(
                model_name="gpt-3.5-turbo",
                avg_response_time=1.5,
                cost_per_1k_tokens=0.002,
                accuracy_score=0.85,
                context_limit=4096
            ),
            "claude-3-opus": ModelPerformanceMetrics(
                model_name="claude-3-opus",
                avg_response_time=10.0,
                cost_per_1k_tokens=0.015,
                accuracy_score=0.96,
                context_limit=200000
            ),
            "claude-3-sonnet": ModelPerformanceMetrics(
                model_name="claude-3-sonnet",
                avg_response_time=3.0,
                cost_per_1k_tokens=0.003,
                accuracy_score=0.90,
                context_limit=200000
            ),
            "claude-3-haiku": ModelPerformanceMetrics(
                model_name="claude-3-haiku",
                avg_response_time=0.8,
                cost_per_1k_tokens=0.00025,
                accuracy_score=0.82,
                context_limit=200000
            )
        }

    def calculate_cost_effectiveness(self,
                                   model_name: str,
                                   expected_tokens_per_request: int,
                                   requests_per_day: int) -> Dict[str, float]:
        """Calculate cost-effectiveness metrics."""

        metrics = self.model_metrics[model_name]

        # Daily costs
        daily_token_cost = (expected_tokens_per_request * requests_per_day *
                           metrics.cost_per_1k_tokens / 1000)

        # Monthly costs
        monthly_cost = daily_token_cost * 30

        # Effectiveness score (accuracy per dollar)
        effectiveness = metrics.accuracy_score / daily_token_cost if daily_token_cost > 0 else 0

        return {
            "daily_cost": daily_token_cost,
            "monthly_cost": monthly_cost,
            "cost_effectiveness": effectiveness,
            "avg_response_time": metrics.avg_response_time,
            "accuracy_score": metrics.accuracy_score
        }

    def recommend_best_model(self,
                           expected_tokens: int,
                           daily_requests: int,
                           max_monthly_budget: float = None,
                           max_response_time: float = None,
                           min_accuracy: float = None) -> List[str]:
        """Recommend models based on constraints."""

        recommendations = []

        for model_name in self.model_metrics.keys():
            metrics = self.calculate_cost_effectiveness(
                model_name, expected_tokens, daily_requests
            )

            # Apply constraints
            if max_monthly_budget and metrics["monthly_cost"] > max_monthly_budget:
                continue

            if max_response_time and metrics["avg_response_time"] > max_response_time:
                continue

            if min_accuracy and metrics["accuracy_score"] < min_accuracy:
                continue

            recommendations.append({
                "model": model_name,
                "metrics": metrics
            })

        # Sort by cost-effectiveness
        recommendations.sort(key=lambda x: x["metrics"]["cost_effectiveness"], reverse=True)

        return [rec["model"] for rec in recommendations]

# Example usage
optimizer = ModelOptimizer()

# Scenario: Customer support chatbot
support_recommendations = optimizer.recommend_best_model(
    expected_tokens=500,      # Average tokens per interaction
    daily_requests=1000,      # 1000 support requests per day
    max_monthly_budget=200,   # $200/month budget
    max_response_time=3.0,    # Must respond within 3 seconds
    min_accuracy=0.80         # Minimum 80% accuracy
)

print(f"Best models for customer support: {support_recommendations}")

# Scenario: Complex document analysis
analysis_recommendations = optimizer.recommend_best_model(
    expected_tokens=2000,     # Longer analysis tasks
    daily_requests=50,        # Lower volume
    max_monthly_budget=500,   # Higher budget for quality
    max_response_time=15.0,   # Can wait longer for quality
    min_accuracy=0.90         # Need high accuracy
)

print(f"Best models for document analysis: {analysis_recommendations}")

Provider-Specific Considerations

OpenAI Models

def configure_openai_model(use_case: str) -> tb.ChatBot:
    """Configure OpenAI models for different use cases."""

    if use_case == "production_chatbot":
        return (tb.ChatBot()
            .model("gpt-4-turbo")  # Best balance for production
            .temperature(0.7)      # Balanced creativity
            .max_tokens(1000))     # Reasonable response length

    elif use_case == "creative_assistant":
        return (tb.ChatBot()
            .model("gpt-4")        # Best creative capabilities
            .temperature(0.9)      # High creativity
            .max_tokens(2000))     # Longer creative responses

    elif use_case == "high_volume_support":
        return (tb.ChatBot()
            .model("gpt-3.5-turbo") # Cost-effective
            .temperature(0.3)       # Consistent responses
            .max_tokens(500))       # Concise answers

    elif use_case == "complex_analysis":
        return (tb.ChatBot()
            .model("gpt-4")        # Best reasoning
            .temperature(0.2)      # Focused and analytical
            .max_tokens(3000))     # Detailed analysis

# Usage examples
creative_bot = configure_openai_model("creative_assistant")
support_bot = configure_openai_model("high_volume_support")

Anthropic Claude Models

def configure_claude_model(document_length: str, priority: str) -> tb.ChatBot:
    """Configure Claude models based on document length and priority."""

    if document_length == "long" and priority == "quality":
        return (tb.ChatBot()
            .model("claude-3-opus")    # Best for long documents
            .temperature(0.4)          # Balanced
            .max_tokens(4000))         # Comprehensive responses

    elif document_length == "medium" and priority == "speed":
        return (tb.ChatBot()
            .model("claude-3-sonnet")  # Good balance
            .temperature(0.5)
            .max_tokens(2000))

    elif priority == "cost":
        return (tb.ChatBot()
            .model("claude-3-haiku")   # Most cost-effective
            .temperature(0.6)
            .max_tokens(1000))

    else:
        return (tb.ChatBot()
            .model("claude-3-sonnet")  # Default balanced choice
            .temperature(0.5)
            .max_tokens(2000))

# Document analysis examples
legal_doc_bot = configure_claude_model("long", "quality")
email_bot = configure_claude_model("short", "speed")
bulk_processing_bot = configure_claude_model("medium", "cost")

Google Gemini Models

def configure_gemini_model(task_type: str) -> tb.ChatBot:
    """Configure Gemini models for different task types."""

    if task_type == "multimodal":
        return (tb.ChatBot()
            .model("gemini-pro-vision")  # For image + text
            .temperature(0.6)
            .max_tokens(2000))

    elif task_type == "rapid_response":
        return (tb.ChatBot()
            .model("gemini-flash")       # Fastest option
            .temperature(0.4)
            .max_tokens(1000))

    else:
        return (tb.ChatBot()
            .model("gemini-pro")         # General purpose
            .temperature(0.7)
            .max_tokens(1500))

# Task-specific bots
image_analyzer = configure_gemini_model("multimodal")
quick_qa_bot = configure_gemini_model("rapid_response")

Dynamic Model Selection

Context-Aware Model Switching

class AdaptiveModelSelector:
    """Dynamically select models based on context and performance."""

    def __init__(self):
        self.performance_history = {}
        self.fallback_models = ["gpt-3.5-turbo", "claude-3-haiku"]

    def select_model(self,
                    message_length: int,
                    complexity_score: float,
                    response_time_requirement: float,
                    conversation_history: tb.Conversation = None) -> str:
        """Select optimal model based on context."""

        # Analyze conversation complexity
        if conversation_history:
            context_complexity = self._analyze_conversation_complexity(conversation_history)
        else:
            context_complexity = 0.5  # Default

        # Combine factors
        overall_complexity = (complexity_score + context_complexity) / 2

        # Model selection logic
        if overall_complexity > 0.8 and response_time_requirement > 5.0:
            return "gpt-4"  # High complexity, can wait
        elif overall_complexity > 0.6 and message_length > 1000:
            return "claude-3-opus"  # Long text analysis
        elif response_time_requirement < 2.0:
            return "gpt-3.5-turbo"  # Speed priority
        elif overall_complexity > 0.7:
            return "gpt-4-turbo"  # Balanced quality/speed
        else:
            return "claude-3-sonnet"  # Default balanced choice

    def _analyze_conversation_complexity(self, conversation: tb.Conversation) -> float:
        """Analyze conversation complexity (simplified)."""
        messages = conversation.get_messages()

        # Factors that increase complexity
        factors = []

        # Length factor
        avg_length = sum(len(msg.content) for msg in messages) / len(messages)
        length_factor = min(avg_length / 500, 1.0)  # Normalize to 0-1
        factors.append(length_factor)

        # Technical terms factor (simplified)
        technical_terms = ["algorithm", "optimize", "analyze", "complex", "detailed"]
        total_text = " ".join(msg.content for msg in messages).lower()
        tech_factor = sum(1 for term in technical_terms if term in total_text) / len(technical_terms)
        factors.append(tech_factor)

        # Question complexity factor
        questions = [msg for msg in messages if "?" in msg.content]
        question_factor = min(len(questions) / len(messages), 1.0)
        factors.append(question_factor)

        return sum(factors) / len(factors)

class SmartChatBot:
    """ChatBot with adaptive model selection."""

    def __init__(self):
        self.model_selector = AdaptiveModelSelector()
        self.current_model = None
        self.current_bot = None

    def chat(self,
             message: str,
             conversation: tb.Conversation = None,
             response_time_requirement: float = 5.0) -> tb.Conversation:
        """Chat with adaptive model selection."""

        # Analyze message complexity
        complexity_score = self._estimate_complexity(message)

        # Select optimal model
        selected_model = self.model_selector.select_model(
            message_length=len(message),
            complexity_score=complexity_score,
            response_time_requirement=response_time_requirement,
            conversation_history=conversation
        )

        # Create or update bot if model changed
        if selected_model != self.current_model:
            self.current_model = selected_model
            self.current_bot = (tb.ChatBot()
                .model(selected_model)
                .temperature(0.7)
                .max_tokens(2000))

            print(f"Switched to model: {selected_model}")

        # Make the chat call
        try:
            return self.current_bot.chat(message, conversation=conversation)
        except Exception as e:
            # Fallback to simpler model
            print(f"Error with {selected_model}, falling back...")
            fallback_bot = (tb.ChatBot()
                .model("gpt-3.5-turbo")
                .temperature(0.7)
                .max_tokens(2000))

            return fallback_bot.chat(message, conversation=conversation)

    def _estimate_complexity(self, message: str) -> float:
        """Estimate message complexity (simplified)."""
        factors = []

        # Length factor
        length_factor = min(len(message) / 1000, 1.0)
        factors.append(length_factor)

        # Technical terms
        technical_indicators = [
            "analyze", "optimize", "algorithm", "architecture", "implement",
            "strategy", "complex", "detailed", "comprehensive", "evaluate"
        ]
        tech_count = sum(1 for term in technical_indicators if term.lower() in message.lower())
        tech_factor = min(tech_count / 5, 1.0)
        factors.append(tech_factor)

        # Question complexity
        if "how" in message.lower() or "why" in message.lower():
            factors.append(0.7)
        elif "what" in message.lower():
            factors.append(0.4)

        return sum(factors) / len(factors) if factors else 0.3

# Usage example
smart_bot = SmartChatBot()

# Simple question - will use fast model
simple_response = smart_bot.chat(
    "What is Python?",
    response_time_requirement=1.0
)

# Complex analysis - will use powerful model
complex_response = smart_bot.chat(
    "Analyze the architectural patterns in this large codebase and recommend optimization strategies for scalability and maintainability",
    response_time_requirement=10.0
)

Testing and Evaluation

Model Performance Comparison

import time
from typing import List, Dict, Any

class ModelComparator:
    """Compare performance across different models."""

    def __init__(self, test_cases: List[str]):
        self.test_cases = test_cases
        self.results = {}

    def evaluate_model(self, model_name: str, persona: str = None) -> Dict[str, Any]:
        """Evaluate a single model across test cases."""

        bot = tb.ChatBot().model(model_name)
        if persona:
            bot = bot.persona(persona)

        results = {
            "model": model_name,
            "response_times": [],
            "response_lengths": [],
            "responses": []
        }

        for test_case in self.test_cases:
            start_time = time.time()

            try:
                response = bot.chat(test_case)
                response_time = time.time() - start_time
                response_content = response.get_last_message().content

                results["response_times"].append(response_time)
                results["response_lengths"].append(len(response_content))
                results["responses"].append(response_content)

            except Exception as e:
                results["response_times"].append(None)
                results["response_lengths"].append(None)
                results["responses"].append(f"ERROR: {str(e)}")

        # Calculate summary statistics
        valid_times = [t for t in results["response_times"] if t is not None]
        valid_lengths = [l for l in results["response_lengths"] if l is not None]

        results["avg_response_time"] = sum(valid_times) / len(valid_times) if valid_times else None
        results["avg_response_length"] = sum(valid_lengths) / len(valid_lengths) if valid_lengths else None
        results["success_rate"] = len(valid_times) / len(self.test_cases)

        return results

    def compare_models(self, models: List[str], persona: str = None) -> Dict[str, Any]:
        """Compare multiple models."""

        comparison_results = {}

        for model in models:
            print(f"Evaluating {model}...")
            comparison_results[model] = self.evaluate_model(model, persona)

        return comparison_results

    def generate_report(self, comparison_results: Dict[str, Any]) -> str:
        """Generate a comparison report."""

        report = "# Model Comparison Report\n\n"

        # Summary table
        report += "## Summary\n\n"
        report += "| Model | Avg Response Time | Avg Length | Success Rate |\n"
        report += "|-------|------------------|------------|---------------|\n"

        for model, results in comparison_results.items():
            avg_time = f"{results['avg_response_time']:.2f}s" if results['avg_response_time'] else "N/A"
            avg_length = f"{results['avg_response_length']:.0f}" if results['avg_response_length'] else "N/A"
            success_rate = f"{results['success_rate']*100:.1f}%"

            report += f"| {model} | {avg_time} | {avg_length} | {success_rate} |\n"

        # Detailed results
        report += "\n## Detailed Results\n\n"

        for i, test_case in enumerate(self.test_cases):
            report += f"### Test Case {i+1}: {test_case[:50]}...\n\n"

            for model, results in comparison_results.items():
                response = results['responses'][i]
                time_taken = results['response_times'][i]

                if time_taken:
                    report += f"**{model}** ({time_taken:.2f}s):\n"
                    report += f"{response[:200]}...\n\n"
                else:
                    report += f"**{model}**: {response}\n\n"

        return report

# Example usage
test_cases = [
    "Explain the concept of machine learning in simple terms",
    "Write a Python function to calculate fibonacci numbers",
    "Analyze the pros and cons of microservices architecture",
    "Create a marketing strategy for a new AI product"
]

comparator = ModelComparator(test_cases)

# Compare models for code assistance
code_models = ["gpt-4", "gpt-4-turbo", "claude-3-sonnet"]
code_results = comparator.compare_models(code_models, persona="senior developer")

# Generate report
report = comparator.generate_report(code_results)
print(report)

Best Practices for Model Selection

1. Start with Balanced Models

Begin with balanced models like gpt-4o or claude-3-sonnet and optimize from there:

# Good starting point for most applications
default_bot = (tb.ChatBot()
    .model("gpt-3.5-turbo")  # Reliable and widely available
    .temperature(0.7)        # Balanced creativity
    .max_tokens(2000))       # Reasonable response length

2. Use Task-Specific Optimization

Optimize models for specific use cases:

# Code review: Accuracy over speed
code_bot = (tb.ChatBot()
    .model("gpt-4")
    .temperature(0.3)
    .max_tokens(3000))

# Customer support: Speed over complexity
support_bot = (tb.ChatBot()
    .model("gpt-3.5-turbo")
    .temperature(0.4)
    .max_tokens(1000))

3. Implement Fallback Strategies

Always have fallback models for reliability:

def create_resilient_bot(primary_model: str, fallback_model: str = "gpt-3.5-turbo"):
    """Create bot with fallback strategy."""

    def chat_with_fallback(message: str, **kwargs):
        try:
            primary_bot = tb.ChatBot().model(primary_model)
            return primary_bot.chat(message, **kwargs)
        except Exception as e:
            print(f"Primary model failed: {e}, falling back to {fallback_model}")
            fallback_bot = tb.ChatBot().model(fallback_model)
            return fallback_bot.chat(message, **kwargs)

    return chat_with_fallback

4. Monitor and Optimize

Continuously monitor model performance:

# Track model performance
performance_tracker = {
    "gpt-4": {"total_requests": 0, "total_time": 0, "errors": 0},
    "gpt-3.5-turbo": {"total_requests": 0, "total_time": 0, "errors": 0}
}

def track_model_performance(model: str, response_time: float, success: bool):
    """Track model performance metrics."""
    performance_tracker[model]["total_requests"] += 1
    performance_tracker[model]["total_time"] += response_time

    if not success:
        performance_tracker[model]["errors"] += 1

Troubleshooting Common Issues

Rate Limiting

def handle_rate_limits(bot: tb.ChatBot, message: str, max_retries: int = 3):
    """Handle rate limiting with exponential backoff."""

    for attempt in range(max_retries):
        try:
            return bot.chat(message)
        except Exception as e:
            if "rate limit" in str(e).lower() and attempt < max_retries - 1:
                wait_time = 2 ** attempt  # Exponential backoff
                time.sleep(wait_time)
                continue
            else:
                raise e

Context Length Issues

def handle_context_length(message: str, conversation: tb.Conversation, max_tokens: int = 4000):
    """Handle context length by truncating conversation history."""

    if not conversation:
        return conversation

    # Estimate tokens (rough approximation)
    total_tokens = len(message.split()) + sum(len(msg.content.split()) for msg in conversation.get_messages())

    if total_tokens > max_tokens:
        # Keep only recent messages
        messages = conversation.get_messages()
        truncated_conv = tb.Conversation()

        # Always keep system message if present
        if messages and messages[0].role == "system":
            truncated_conv.add_system_message(messages[0].content)
            messages = messages[1:]

        # Add recent messages until we approach the limit
        recent_messages = []
        current_tokens = len(message.split())

        for msg in reversed(messages):
            msg_tokens = len(msg.content.split())
            if current_tokens + msg_tokens < max_tokens * 0.8:  # Leave some buffer
                recent_messages.insert(0, msg)
                current_tokens += msg_tokens
            else:
                break

        # Add messages to conversation
        for msg in recent_messages:
            if msg.role == "user":
                truncated_conv.add_user_message(msg.content)
            elif msg.role == "assistant":
                truncated_conv.add_assistant_message(msg.content)

        return truncated_conv

    return conversation

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Key Takeaways

• Match models to tasks - Use complex models for complex tasks, simple models for simple tasks
• Consider the full cost - Factor in response time, API costs, and accuracy requirements
• Start balanced - Begin with models like GPT-4 Turbo or Claude-3 Sonnet
• Implement fallbacks - Always have backup models for reliability
• Monitor performance - Track metrics and optimize based on real usage
• Test thoroughly - Compare models on your specific use cases before production